Feature based three-dimensional object recognition using disparity maps

The human vision system is able to recognize objects it has seen before even if the particular orientation of the object being viewed was not specifically seen before. This is due to the adaptability of the cognitive abilities of the human brain to categorize objects by different features. The features and experience used in the human recognition system are also applicable to a computer recognition system. The recognition of three-dimensional objects has been a popular area in computer vision research in recent years, as computer and machine vision is becoming more abundant in areas such as surveillance and product inspection. The purpose of this study is to explore and develop an adaptive computer vision based recognition system which can recognize 3D information of an object from a limited amount of training data in the form of disparity maps. Using this system, it should be possible to recognize an object in many different orientations, even if the specific orientation had not been seen before, as well as distinguish between different objects

View synthesis for depth from motion 3D x-ray imaging.

The depth from motion or kinetic depth X-ray imaging (KDEX) technique is designed to enhance the luggage screening at airport checkpoints. The technique requires multiple views of the luggage to be obtained from an arrangement of linear X-ray detector arrays. This research investigated a solution to the unique problems defined when considering the possibility of replacing some of the X-ray sensor views with synthetic images. If sufficiently high quality synthetic images can be generated then intermediary X-ray sensors can be removed to minimise the hardware requirements and improve the commercial viability of the KDEX technique. Existing image synthesis algorithms are developed for visible light images. Due to fundamental differences between visible light and X-ray images, those algorithms are not directly applicable to the X-ray scenario. The conditions imposed by the X-ray images have instigated the original research and novel algorithm development and experimentation that form the body of this work. A voting based dual criteria multiple X-ray images synthesis algorithm (V-DMX) is proposed to exploit the potential of two matching criteria and information contained in a sequence of images. The V-DMX algorithm is divided into four stages

3D RECONSTRUCTION FROM STEREO/RANGE IMAGES

3D reconstruction from stereo/range image is one of the most fundamental and extensively researched topics in computer vision. Stereo research has recently experienced somewhat of a new era, as a result of publically available performance testing such as the Middlebury data set, which has allowed researchers to compare their algorithms against all the state-of-the-art algorithms. This thesis investigates into the general stereo problems in both the two-view stereo and multi-view stereo scopes. In the two-view stereo scope, we formulate an algorithm for the stereo matching problem with careful handling of disparity, discontinuity and occlusion. The algorithm works with a global matching stereo model based on an energy minimization framework. The experimental results are evaluated on the Middlebury data set, showing that our algorithm is the top performer. A GPU approach of the Hierarchical BP algorithm is then proposed, which provides similar stereo quality to CPU Hierarchical BP while running at real-time speed. A fast-converging BP is also proposed to solve the slow convergence problem of general BP algorithms. Besides two-view stereo, ecient multi-view stereo for large scale urban reconstruction is carefully studied in this thesis. A novel approach for computing depth maps given urban imagery where often large parts of surfaces are weakly textured is presented. Finally, a new post-processing step to enhance the range images in both the both the spatial resolution and depth precision is proposed

MRF Stereo Matching with Statistical Estimation of Parameters

For about the last ten years, stereo matching in computer vision has been treated as a combinatorial optimization problem. Assuming that the points in stereo images form a Markov Random Field (MRF), a variety of combinatorial optimization algorithms has been developed to optimize their underlying cost functions. In many of these algorithms, the MRF parameters of the cost functions have often been manually tuned or heuristically determined for achieving good performance results. Recently, several algorithms for statistical, hence, automatic estimation of the parameters have been published. Overall, these algorithms perform well in labeling, but they lack in performance for handling discontinuity in labeling along the surface borders. In this dissertation, we develop an algorithm for optimization of the cost function with automatic estimation of the MRF parameters – the data and smoothness parameters. Both the parameters are estimated statistically and applied in the cost function with support of adaptive neighborhood defined based on color similarity. With the proposed algorithm, discontinuity handling with higher consistency than of the existing algorithms is achieved along surface borders. The data parameters are pre-estimated from one of the stereo images by applying a hypothesis, called noise equivalence hypothesis, to eliminate interdependency between the estimations of the data and smoothness parameters. The smoothness parameters are estimated applying a combination of maximum likelihood and disparity gradient constraint, to eliminate nested inference for the estimation. The parameters for handling discontinuities in data and smoothness are defined statistically as well. We model cost functions to match the images symmetrically for improved matching performance and also to detect occlusions. Finally, we fill the occlusions in the disparity map by applying several existing and proposed algorithms and show that our best proposed segmentation based least squares algorithm performs better than the existing algorithms. We conduct experiments with the proposed algorithm on publicly available ground truth test datasets provided by the Middlebury College. Experiments show that results better than the existing algorithms’ are delivered by the proposed algorithm having the MRF parameters estimated automatically. In addition, applying the parameter estimation technique in existing stereo matching algorithm, we observe significant improvement in computational time

Research on a modifeied RANSAC and its applications to ellipse detection from a static image and motion detection from active stereo video sequences

制度:新 ; 報告番号:甲3091号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2010/2/24 ; 早大学位記番号:新535

A Layered Stereo Algorithm Using Image Segmentation And Global Visibility Constraints

We propose a new stereo algorithm which uses colour segmentation to allow the handling of large untextured regions and precise localization of depth boundaries. Each segment is modelled as a plane. Robustness of the depth representation is achieved by the use of a layered model. Layers are extracted by mean-shift-based clustering of depth planes. For layer assignment a global cost function is defined. The quality of the disparity map is measured by warping the reference image to the second view and comparing it with the real image. Z-buffering enforces visibility and allows the explicit detection of occlusions. An efficient greedy algorithm searches for a local minimum of the cost function. Layer extraction and assignment are alternately applied. Results obtained for benchmark and self-recorded images indicate that the proposed algorithm can compete with the state-of-the-art

An assessment model and implementation of stereo image quality

In the past decade, many display hardware manufacturers have initiated research into the construction of stereo display devices. Currently, the use of such displays is limited to the computer-aided design; research, military and medical applications. However, it is anticipated that as display hardware becomes cheaper, gaming companies and desktop application software developers will realise the potential of using stereo to provide more realistic user experiences. To provide realistic stereo user experience it is necessary to utilise good quality stereo images in addition to suitable hardware. The growth of the Internet has resulted in an increase in the availability of stereo images. However, most have been captured using uncontrolled procedures and have questionable quality. The quality of stereo images is important since the viewing of poor quality stereo images can result in adverse viewing effects. A formal definition of stereo quality has not been achieved in current day research. This means that the factors which cause a stereo image to be perceived as poor quality have not been defined nor is a system available to detect its occurrence. This thesis attempts to address this problem by postulating a definition of stereo image quality based on detecting level of excess disparity levels, intensity differences and the occurrence of frame cancellation. An implementation system able to detect these identified factors is discussed and formulated. The developed system is utilised to test 14 stereo images of varying quality levels. The results of these tests are reported and are used to evaluated and refine the system. Using this image analysis, benchmarks for natural intensity difference in images, changes due to JPEG compression and comparisons with generated and ground truth disparity maps are formulated. Additionally, a